Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: how zones are derived (clustered swing highs/lows), what touch counts mean, and that results include nearest levels above/below with a distance metric.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the action and the output inventory, followed by a short usage sentence. Dense but every clause carries information; no filler sentences, though the parenthetical touch-count explanation makes the first sentence long.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-value detail is not required, and the description adequately covers what is computed and why. Minor gaps remain around data freshness or behavior for non-equity tickers, but nothing essential for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With a single parameter and 100% schema description coverage, the schema already documents 'ticker' including an example. The description adds no additional semantics about the ticker argument, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('compute') and resource ('key price levels for a ticker'), then enumerates exactly what is produced: support/resistance zones with touch counts, round numbers, 50/200-day MAs, and 52-week high/low. That enumeration functionally distinguishes it from siblings like get_technical_indicators or get_price_history without needing to name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear use case — 'Use these for concrete trigger/target/invalidation levels' — which tells the agent the intended decision context. It does not, however, state when to prefer this over get_technical_indicators or get_price_history, so no explicit exclusions or alternatives are offered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.